OPTIMISATION OF INJECTION MOULDED POLYPROPYLENE SAWDUST COMPOSITE USING RESPONSE SURFACE METHODOLOGY AND ARTIFICIAL NEURAL NETWORKS

Year 2020, Volume: 2 Issue: 3, 169 - 177, 30.11.2020

Cyril Aliyegbenoma Mercy Ozakpolor

https://doi.org/10.47933/ijeir.744495

Cited By: 2

Abstract

This study focuses on the optimisation of the injection moulded Polypropylene -Sawdust (PP-sawdust) composite. The PP material and sawdust were mixed together to form a homogenous mixture with various percentage composition by volume as recommended by the design of experiments using the central composite design (CCD). The two screw plunger injection moulding machine was used to produce Polypropylene-Sawdust (PP-Sawdust) composite at various temperature. The produced composites were evaluated for their mechanical properties which included tensile strength, proof stress, percentage elongation and flexural strength. The response surface methodology (RSM) and artificial neural networks (ANN) were used to determine the effect of the interaction of temperature, material type and percentage by volume of material on the mechanical properties of the produced PP-sawdust composite. The models were validated using coefficient of determination (R2).
The models were validated using coefficient of determination (R2). The coefficient of determination (R2) obtained ranged from 0.9435 (94.357%) to 0.9988 (99.88%) which indicates that a substantial good fit was achieved by the model developed. A desirability of 0.952 was obtained which shows the adequacy of the model terms The optimization results for PP-Sawdust composites shows that the tensile strength, proof stress, flexural strength and flexural modulus were maximized with values of 31.90 MPa, 41.94 MPa, 88.22 MPa and 2.72 GPa respectively obtained at barrel temperature of 224.65 oC and polymer level of 45.56% while percentage elongation and average deflection were minimized with values of 74.12% and 6.46 cm respectively

Keywords

Central composite design, Composite, Mahogany, Modeling, Polypropylene, Sawdust, tensile strength

References

• [1], József, G. K; Tibor B (2005). Influence of mold properties on the quality of injection molded parts, Periodica Polytechnica Ser. Mech. Eng. Vol. 49, No. 2, Pp. 115–122 (2005)
• [2]. Acharya S.K and Mishra S.C, (2007) “Weathering behavior of fly-ash jute polymer composite, Journal of Reinforced Plastics and Composites, vol. 26, no. 12, pp. 1201–1210.
• [3]. Chunping, D., Changing Y. and Cheng Z., (2007). Theoretical modeling of bonding characteristics and performance of wood composites: part 1.inter –element contact.Journal of wood and fiber science, vol.39, pp.48-55.
• [4]. Osarenmwinda, J.O, and Nwachukwu J.C. (2010). Development of Composite Material from Agricultural Waste’.International Journal of Engineering Research in Africa. Volume 3, PP42-48
• [5]. Njoku R.E and Obikwelu D.O.N (2008), swelling characteristics and tensile properties of natural fiber reinforced plastic in selected solvents, Nigerian journal of Technology, vol 27, no2
• [6]. Westerdale, S., kazmer, D. O., Hazen, D .(2008), a comparison of statistical process control (SPC) nand on-line multivariate analyses (MVA) for injection molding, international polymer processing, vol. 23: issue5 pages 447-458
• [7]. Hejazi, S.M., Sheikhzadeh, M., Abtahi, S.M, and Zadhoush A., (2012). A simple review of soil reinforcement by using natural and synthetic fibers, Construction and Building Materials, vol. 30, pp. 100–116.
• [8]. Aliyegbenoma C. O, Eki M. U, Ozakpolor M. O, (2020) Modelling and Production of Injection Moulded Polyproplyn Sawdust Composite, International Journal of Scientific Engineering and Applied Science (IJSEAS) – Vol 6, Pp 1-17
• [9]. Amenaghawon, N. A.; Ogbeide, S. E; Okieimen, C.O (2014). Application of statistical experimental design for the optimisation of dilute sulphuric acid hydrolysis of cassava Bagasse. Acta Polytechnica Hungarica, 11(9), pp. 1-12.
• [10]. Montgomery, D.C (2005). Design and analysis of experiments, 6th ed., New York: John Wiley & Sons, Inc
• [11]. Carley, K..M; Kamneva,, N.Y;. Reminga, J (2004). Response surface methodology. CASOS-center for computational analysis of social and organizational systems technical report, carnegie Mellon University, School of Computer Science, p. 7
• [12]. Saracoglu, O. G. (2008). An artificial neural network approach for the prediction of absorption measurements of an evanescent field fiber sensor. Sensors, 8(3), pp. 1585-1594